ies0411 / readme_baseline

Project Title

Camera Intrinsic Parameter

Index

CPP TYPE

• Dependancy
• Install
• Parameter
  • Demo
• Results
• test

---

CPP TYPE

Dependancy

• ROS melodic ver
• Opencv 3.4 이상
• c++17

Install

1. 직접세팅

** TODO

2. Docker 사용 Docker 설치 및 Nvidia docker Image도 설치필요

$ sudo apt-get install x11-xserver-utils
$ xhost +

$ docker pull authorsoo/px4:9.0
$ docker run --gpus all -it --ipc=host  --expose 22 --net=host --privileged -e DISPLAY=unix$DISPLAY 
    -v /tmp/.X11-unix:/tmp/.X11-unix:rw -e NVIDIA_DRIVER_CAPABILITIES=all --name calib authorsoo/px4:9.0 bash

Parameter

rosrun 이 아닌 launch 파일로 실행시길 경우 직접 파라미터 세팅

$ roslaunch mono_cam_calib mono_calib

Demo

1. Dataset 이용 Dataset

도커 이용시 /home 디렉토리에 파일 존재함

$ rosrun mono_cam_calib mono_cam_calib
$ rosbag play [rosbag 파일명] // rosbag실행

Demo Video

Results

result폴더 Txt파일에 intrinsic, 왜곡, R, T 값들

TODO

파일 형식 txt 에서 Yaml 혹은 Json으로 변경

---

PYTHON Type

** TODO

---

MARKDOWN

MARKDOWN 정리, 실습 for README.md

1. 제목(글머리) 작성

H1 제목

H2 부제목

H3 소제목

H4 제목4

H5 제목5

H6 제목6

2. 번호 없는 리스트 작성

• 리스트1
  • 리스트2
    • 리스트3

3. 번호 있는 리스트 작성

1. 리스트1
2. 리스트2
3. 리스트3

4. 이텔릭체(기울어진 글씨) 작성

텍스트

5. 굵은 글씨 작성

텍스트

6. 인용

인용1

인용2

인용안의 인용

7. 수평선 넣기

---

8. 링크 달기

(1) 인라인 링크

블로그 주소

(2) 참조 링크

블로그 주소

9. 이미지 추가하기

이미지 사이즈 조절

이미지 파일로 추가하기

10. 코드블럭 추가하기

public struct CGSize {
  public var width: CGFloat
  public var heigth: CGFloat
  ...
}

etc

텍스트 굵게
텍스트 취소선

[개행]

스페이스바를 통한 문장개행
스페이스바를 통한 문장개행

br태그를 사용한 문장개행

br태그를 사용한 문장개행

[체크박스]

다음과 같이 체크박스를 표현 할 수 있습니다.

• 체크박스
• 빈 체크박스
• 빈 체크박스

[이모지 넣기]

❤️💜💙🤍

[표 넣기]

왼쪽 정렬	가운데 정렬	오른쪽 정렬
내용1	내용2	내용3
내용1	내용2	내용3

---

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

What things you need to install the software and how to install them

Give examples

Installing

A step by step series of examples that tell you how to get a development env running

Say what the step will be

Give the example

And repeat

until finished

End with an example of getting some data out of the system or using it for a little demo

Running the tests

Explain how to run the automated tests for this system

Break down into end to end tests

Explain what these tests test and why

Give an example

And coding style tests

Explain what these tests test and why

Give an example

Deployment

Add additional notes about how to deploy this on a live system

Built With

• Dropwizard - The web framework used
• Maven - Dependency Management
• ROME - Used to generate RSS Feeds

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

Versioning

We use SemVer for versioning. For the versions available, see the tags on this repository.

Authors

• Billie Thompson - Initial work - PurpleBooth

See also the list of contributors who participated in this project.

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

• Hat tip to anyone whose code was used
• Inspiration
• etc

---

Repository Quick Start template

Index

• Overview
• Getting Started
• Contributing
• Authors
• License

About RepositoryTemplate

This project's purpose is to create a make Repository with a collection of default settings

Overview

If you use this template, you can use this function

• Issue Template
• Pull Request Template
• Commit Template
• Readme Template
• Contribute Template
• Pull Request Build Test(With Github Actions)

Getting Started

click Use this template and use this template!

Installing

1. Click Use this template button
2. Create New Repository
3. Update Readme and Others(Other features are noted in comments.)

Contributing

I am looking for someone to help with this project. Please advise and point out.
Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

Authors

• Always0ne - SangIl Hwang - [e-mail]

See also the list of contributors who participated in this project.

License

MIT License

Copyright (c) 2020 always0ne

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

Siesta

Requirement

• Latest Node.js 14 LTS (recommend you to use nvm or asdf)
• Yarn 2

Gentle Suggestions for Readings

• You know ask good questions. 한국어 비슷한 글
• You are done react official tutorials
• You are done offical Next.js tutorials
• You are good at TypeScript
• You know concepts of Yarn 2
• We use Stitches to style our application
• We prefer flat folders and long file name

Installation

$ yarn

Development

$ yarn dev

# upgrade deps with interactive CUI
$ yarn upgrade-interactive

# update yarn version for this project
$ yarn set version latest

Structure

.
├── jest            # Jest Configurations
├── cypress         # Cypress configurations & tests
├── src
│   ├── apis        # API Fetch functions
│   ├── assets      # Static resources that will be transpiled
│   ├── components  # React components
│   ├── hooks       # React custom hooks
│   ├── itly        # Auto-generated tracking code, see below
│   ├── misc        # Ambiguous little things
│   ├── modes       # Mold Modes
│   ├── pages       # Next.js pages
│   ├── shapes      # Mold Shapes
│   ├── shared      # Core Utility / Interface
└───└── stitches    # Stitches Definition

Itly (Amplitude)

The contents of itly/ are auto-generated by the Amplitude Data CLI (Iteratively).

To update the tracking plan, run ampli pull. Find more information in Notion

Productivity

• We avoid hasty abstraction
• Duplication doesn't matter
• We write test code to save our times and make more value
• Recommend you to read all test posts of Kent
• Don't Solve Problems, Eliminate Them

Deployment

We use Vercel to deploy our project.

• main branch deployed as production: https://suite-anno-v2.superb-ai.com
• stage branch deployed as QA preview (almost same as production): https://stage.suite-anno-v2.superb-ai.com
• develop branch deployed as preview: https://dev.suite-anno-v2.superb-ai.com
• other branches deployed as preview

Other Recommendations

• Thinking in GraphQL
• Thinking in Relay
• React Design Principals

Install

• set env

$ docker build -t mmdetection3d -f docker/Dockerfile .

Set Data

1. Kitti Data

Download KITTI 3D detection data Here. Prepare KITTI data splits by running

mkdir ./data/kitti/ && mkdir ./data/kitti/ImageSets

# Download data split
wget -c  https://raw.githubusercontent.com/traveller59/second.pytorch/master/second/data/ImageSets/test.txt --no-check-certificate --content-disposition -O ./data/kitti/ImageSets/test.txt
wget -c  https://raw.githubusercontent.com/traveller59/second.pytorch/master/second/data/ImageSets/train.txt --no-check-certificate --content-disposition -O ./data/kitti/ImageSets/train.txt
wget -c  https://raw.githubusercontent.com/traveller59/second.pytorch/master/second/data/ImageSets/val.txt --no-check-certificate --content-disposition -O ./data/kitti/ImageSets/val.txt
wget -c  https://raw.githubusercontent.com/traveller59/second.pytorch/master/second/data/ImageSets/trainval.txt --no-check-certificate --content-disposition -O ./data/kitti/ImageSets/trainval.txt

Generate info file

python tools/create_data.py kitti --root-path ./data/kitti --out-dir ./data/kitti --extra-tag kitti

2. Suite Data

• Convert Suite to Kitti Data Format

  reference : custom_data.ipynb in https://github.com/Superb-AI-Suite/voda.git

  [TODO] Make a script to download asset / label from suite project with login information.

• Debug : Kitti_GT_3Dbbox_visualization.ipynb in https://github.com/Superb-AI-Suite/voda.git

• Generate info file

python tools/create_data.py kitti --root-path ./data/superb --out-dir ./data/superb --extra-tag kitti

Demo

1. kitti

python tool/train.py configs/configs/parta2/hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-car.py

2. Suite

python tool/train.py configs/configs/superb/custom.py

or Using train_demo.ipnb

Main Config

• reference : /config/superb/custom.py

• dataset_type : select in [Kitti, cityspace, waymo, nuscenes], Our code based on Kitti

• data_root = 'data/superb/', custom data(suite -> kitti)

• point_cloud_range : velodyne coordinates, x, y, z

• input_modality : use_lidar=True, use_camera=False

• resume_from : load pretrained model

• checkpoint_config = set interval of saving checkpoint and path

  ex) dict(interval=3, out_dir='/home/eunsoo/dl/mmdetection3d/checkpoints/')

• evaluation : set evalutation metric

  ex) cfg.evaluation.metric = ['bbox', 'segm']

• learning rate, batch size

  ex) cfg.optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)

Error Situation

1. Nothing PCD in BBox bbox
2. Out of range when appling data augmentation